Unmanned Aerial Vehicle Jamming Resource Scheduling Based on Parallel Genetic Algorithm with Elite Set

DENG Min; WU Zhigao; YAO Zhiqiang; CHEN Yongqi

doi:10.11999/JEIT210349

Volume 44 Issue 6

Jun. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 2158-2165

DENG Min, WU Zhigao, YAO Zhiqiang, CHEN Yongqi. Unmanned Aerial Vehicle Jamming Resource Scheduling Based on Parallel Genetic Algorithm with Elite Set[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2158-2165. doi: 10.11999/JEIT210349

Citation:

DENG Min, WU Zhigao, YAO Zhiqiang, CHEN Yongqi. Unmanned Aerial Vehicle Jamming Resource Scheduling Based on Parallel Genetic Algorithm with Elite Set[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2158-2165. doi: 10.11999/JEIT210349

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Unmanned Aerial Vehicle Jamming Resource Scheduling Based on Parallel Genetic Algorithm with Elite Set

doi: 10.11999/JEIT210349

1.
Science and Technology on Communication Information Security Control Laboratory, No. 36 Research Institute of China Electronics Technology Group Corporation, Jiaxing 314000, China
2.
School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China

Funds: The Provincial Natural Science Foundation of Hunan (2019JJ50620), The National Key R&D Program of China (2020YFA0713502), The National Science Key Laboratory Foundation (61421060404)

Received Date: 2021-04-23
Rev Recd Date: 2021-10-12

Available Online: 2022-04-17

Publish Date: 2022-06-21

Abstract

Abstract

In order to solve the optimization problem of jamming resource scheduling in medium and large-scale Unmanned Aerial Vehicle (UAV) jamming scenarios, a jamming resource scheduling model that can meet the minimum number of tasks constraint is proposed to improve the simple constraints and small-scale solution algorithms of the existing models. The interference benefit and cost indicators are weighted by the analytic hierarchy process. Then an improved parallel genetic algorithm is designed, where the elite set is introduced to accelerate the convergence of the algorithm. The simulation results in medium scale and larger scale jamming situations, such as 500:500 (number of jamming resources: number of targets) show that the proposed algorithm converges faster and achieves better objective function value than the existing representative and improved genetic algorithms.
- Interference countermeasure,
- Resource scheduling,
- Genetic algorithm,
- Parallel algorithm,
- Hybrid mode

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References(25)

References

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